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GTM Feedback provides AI-powered analytics to help you understand customer feedback patterns, prioritize features, and make data-driven product decisions.

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Area Insights Reports

Each product area automatically generates comprehensive AI insights.

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What Gets Analyzed

The insights engine analyzes:
  • All feature requests in the area
  • Customer feedback linked to those requests
  • Account data (ARR, enterprise status, region)
  • Opportunity data (stage, close date, deal value)
  • Request metadata (status, severity distribution)
  • Trends over time (feedback velocity, pattern changes)

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Insight Sections

Reports are organized into sections, each containing:

Title

Clear description of the theme or pattern identified.

Summary

Detailed explanation of what the analysis found.

Importance

Rating: Critical, High, Medium, or Low priority.

Visuals

Charts and metrics supporting the insight.

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Example Insight Section

Title: "Enterprise customers requesting advanced permissions"

Summary: "15 enterprise accounts with $2.3M combined ARR have 
requested granular role-based access controls. 3 opportunities 
($850K ARR) cite this as a requirement for deal closure."

Importance: High

Visuals:
- Top 5 requests by opportunity ARR
- Dealbreaker count (3 critical)
- Severity distribution chart

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Accessing Insights

View area insights on the area page:
1

Navigate to area

Go to /areas/[slug] for any product area.
2

Expand analytics

Click the “Analytics” section to expand the insights panel.
3

Review sections

Read through each insight section and associated data.
4

Regenerate if needed

Click “Regenerate” to create fresh insights with latest data.
Insights are cached and regenerated weekly via automated cron job. You can manually regenerate anytime.

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Insight Visualizations

Reports include multiple visualization types:

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Top Requests Chart

Shows the highest-impact feature requests:
  • Ranked by open opportunity ARR
  • Includes request title and metrics
  • Clickable links to full request pages
  • Displays severity distribution

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Dealbreaker Metrics

Highlights critical feedback:
  • Count of “high severity” feedback items
  • Opportunities at risk
  • Total ARR depending on these features
  • Customer segments most affected

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Crunches Numbers Section

Raw metrics and data:
  • Total feature requests
  • Status breakdown (open/shipped/deprioritized)
  • Average feedback per request
  • Requests with high-severity feedback

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Weekly Digests

Automated weekly summaries sent to your team via Slack.

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Digest Contents

Each weekly digest includes:
The 3 areas with most activity:
  • Request count for the week
  • New feedback items
  • Links to area pages
All requests created during the week:
  • Request title and link
  • Creator name
  • Assigned product areas
Recent customer feedback highlights:
  • Account name
  • Feature request title
  • Link to view details
  • Unique accounts (deduplicated)
Detailed breakdown per area:
  • Total requests in area
  • New feedback this week
  • Top movers (most active areas)

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Digest Schedule

Digests are sent:
  • Automatically every Monday morning
  • Via Slack to configured channel
  • With clickable links to all referenced items
  • Only when there’s activity (no empty digests)

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Configuring Digests

Set up weekly digests:
  1. Configure SLACK_DIGEST_CHANNEL_ID environment variable
  2. Point to your team’s digest channel (e.g., #product-digest)
  3. Digests will post automatically on schedule
Digests use the weekly-digest cron workflow. See /api/cron/weekly-digest for implementation.

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Dashboard Features

The main dashboard provides overview analytics:

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Request Overview

  • Total requests across all areas
  • Status distribution (pie chart)
  • Recent activity (timeline)
  • Top areas by volume (bar chart)

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Feedback Overview

  • Total feedback items collected
  • Severity breakdown (stacked chart)
  • Top accounts by feedback count
  • Feedback trends over time

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Business Impact

  • Total ARR from engaged accounts
  • Opportunity pipeline linked to requests
  • Enterprise engagement metrics
  • Regional distribution of feedback

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AI Generation Process

How insights are created:
1

Data collection

System gathers all requests, feedback, and CRM data for the area.
2

Analysis

AI model analyzes patterns, themes, and business impact:
  • Clusters similar requests
  • Identifies high-value patterns
  • Calculates importance scores
  • Detects trends and anomalies
3

Visualization generation

Creates charts and metrics:
  • Top requests ranking
  • Dealbreaker counts
  • ARR distribution
  • Severity breakdowns
4

Report assembly

Combines sections into coherent report:
  • Orders by importance
  • Links to supporting data
  • Generates readable summaries
  • Caches for fast access
Insight generation requires sufficient data. Areas with fewer than 3 requests may not generate meaningful insights.

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Insights Feedback

Help improve the AI insights:

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Rating Insights

At the bottom of each insight report:
  1. Thumbs up - Insights were helpful
  2. Thumbs down - Insights need improvement
  3. Optional comment - Explain what was good/bad

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What Happens with Feedback

Your feedback:
  • Posts to internal Slack channel
  • Includes your name and area
  • Helps tune the AI model
  • Informs future improvements
Be specific in comments: “Missing key customer: Acme Corp” or “Great catch on the authentication theme!”

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Prioritization Metrics

Use these metrics to prioritize features:

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ARR-Based Priority

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Open Opportunity ARR
number
Revenue at risk if feature isn’t delivered. Direct signal of urgency.Use for: Deal-driven prioritization
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Account ARR
number
Total revenue from customers requesting this. Shows market demand.Use for: Strategic prioritization

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Volume-Based Priority

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Feedback Count
number
How many customers are asking. Indicates breadth of need.Use for: Popular demand signals
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High Severity Count
number
Number of critical/urgent requests. Shows pain level.Use for: Customer satisfaction urgency

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Strategic Priority

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Enterprise Count
number
Number of enterprise customers requesting. Strategic account focus.Use for: Enterprise customer retention
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Opportunity Close Date
date
Nearest opportunity close date. Creates timeline urgency.Use for: Sales deadline prioritization

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Best Practices

Make area insights part of your planning:
  • Check before sprint planning
  • Review with product leads
  • Share with engineering teams
  • Track changes over time
Don’t rely on single metrics:
  • High ARR + High severity = Critical
  • High volume + Low ARR = Nice-to-have
  • Enterprise + Close deadline = Urgent
  • Low all metrics = Deprioritize
Get fresh insights when:
  • Planning quarterly roadmaps
  • Preparing for stakeholder reviews
  • Evaluating major initiatives
  • Responding to market changes
Help improve the AI:
  • Rate every report you read
  • Leave specific comments
  • Note missing patterns
  • Highlight particularly useful sections
Leverage weekly digests:
  • Post in team standups
  • Include in planning docs
  • Forward to stakeholders
  • Track trends week-over-week

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API Access

Programmatic access to insights: 
# Get area insights
GET /api/areas/[slug]/insights

# Get raw analytics data  
GET /api/areas/[slug]/analytics/raw

# Trigger regeneration
POST /api/areas/[slug]/insights/regenerate
See API Reference for full documentation.

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